Machine-learning optimization for computing networks

ABSTRACT

A machine-learning optimization of a plurality of networks is provided. The machine-learning optimization includes interconnecting an online platform providing a machine learning module, a core network of computers deploying novel software, and a plurality of Internet network service providers. The platform collects, via the software, performance data of the Internet networks, which the machine learning module utilizes to enhance performance and reduce the latency therein networks by taking into account thousands of real-time and historic latency and bandwidth metrics. Thereby the software continually selects an optimal path through the plurality of Internet networks.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. provisionalapplication number 62/301,498, filed 29 Feb. 2016, the contents of whichare herein incorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates to computing networks and, moreparticularly, to a machine-learning optimization of networking basedupon a multitude of performance metrics.

Current computing networks and their associated networking equipment arestatically built to support the humanly estimated need of a givenapplication or throughput requirement. They are also built in a veryproprietary manner, thus forcing the user to use one service providerfor all processes.

As a result, current computing networks and their associated networkingequipment lack elasticity. While many advancements have begun to provideand “on demand” functionality, they do not solve for the currentconstraints on the flow of traffic through the network. Specifically,networking performance capable devices today are limited by simplerouting protocols that are over a decade old. Moreover, the selection ofrouting or path is based on a per device basis, typically requiringproprietary licenses, and otherwise only working on devices of the samemanufacturer.

In private-network services, such as MPLS or VPLS, there are Quality ofService capabilities that classify and prioritize traffic within theWide Area Network Service Providers network, but this is completelywithin the confines of their completely owned and privatized networks.Furthermore, such private networks are predefined and weighted routes orpaths built on a pre-constructed network.

The global Internet provides and even larger problem. Not one serviceprovider can control or guarantee the end to end throughput and securityof traffic across the open Internet. Some Internet provides provideimproved caching and TCP optimization tactics, but the impact isminimal. This is what keeps customers loyal to their chosen privateservice provider networks; however, the drawback to loyalty is thatInternet service providers do not rely on competitor networks forsupport during issues like congestion or outage.

Other networking platforms are completely proprietary. They rely onexisting WAN and Internet infrastructure to follow pre-defined rules,not dynamic or learned applications. And so, if there is congestion onthe internet many VPN or IPSec platforms will not see and are not ableto dynamically react to congestion, packet loss, jitter, or latency. Todate, VPN services see only two points, the origin and destination. As aresult, high costs and complex configuration make these platformscumbersome and undesirable.

All other devices that use VPNs, IPSec, GRE, Auto VPN, or any other siteto site topologies, do not control the performance, latency, speed orquality of the entire end to end Internet path. Some control theselection of what local circuit to traverse, but have no control beyondthat.

The issue of flow traffic through networks will only be acerbated by theexpectations of today's data consumers, who demand the following:elasticity in their networks (when it is hard to predict network needs,for example: twitter, events or seasonal changes); configurabilitycontrol (consumers want to do it ourselves; and now!); data securitymade easy and safe (even though security has become tougher and morecomplex to address); visibility of performance and instant reporting(i.e., consumer wants to know if they are getting what they purchased);and price compression paired with an increase in capacity andimprovement of performance.

As can be seen, there is a need for a machine-learning optimization ofnetworking based upon a multitude of performance metrics, also or attimes impacting equipment configurations. The optimization of trafficflow of the present invention is delivered through the creation of dataflows and device profiles using machine learning adapted to providereal-time application. Unlike other service providers or equipmentmanufacturers today, the present invention empowers the customer todeploy and manage their domestic and global networks and associatedequipment agnostically, yet securely and instantly with little or nocommunications from the provider of the present invention, and whereconsumers can also remove or disconnect at their leisure.

The present invention provides a platform that empowers the user toinstantly stand up a secure and reliable domestic or global network,thereby bypassing the “brick and mortar” service provider model andlengthy timeframes to deploy. The empowering software of the presentinvention may be deployed in a plurality of data centers across theglobe, wherein software defined gateways may be powered by machinelearning artificial intelligence. The software of the present inventioncan also be deployed on local and on-site equipment as well.

The platform utilizes the collected performance data via a machinelearning module to enhance performance and reduce the latency within theInternet, MPLS, VPLS and EoIP networks based on knowledge of the mostcommon destination so as to create the best latency-based optimizedroute to minimize delay for real-time traffic, adding significant valueto the WAN core traffic engineering and RSVP TE management.

Furthermore, the platform provides stronger security through the trafficbehavior profiles that protect the user from anomalies by alerting theuser to change in traffic patterns which could be caused by malware, avirus, or an unauthorized access.

The software of the present invention is adapted to establish a corenetwork among a plurality of deployed data centers. In certainembodiments, endpoints interconnecting the core network with a pluralityof Internet software provides, based upon in part a L2TP over IPSecnegotiation, a machine learning module, which has taken into accountthousands of real-time and historic metrics within less than twoseconds. Through this process of optimizing Internet-based networksbased upon real time and historic machine learned performance metrics,Internet service provider(s) are selected as optimal within the path ofthe initial connection for navigating the specific path of data as ispasses through such service providers.

Once connected to the core network, the customer may be provided withoptions of stand up traditional services, such as MPLS, VPLS, and EoIP,over a completely optimized machine learning core network, leveragingL2TP over IPSec among the plurality of data center connections.

Based on the machine learning module provided through the platform aflow of data may route over 1, 2, 5, or 10+ different Internet serviceproviders based purely upon obtaining the optimal performing route/path.This is a combination of IP transit connectivity coupled with themachine learning functionality, creating an artificially intelligentnetwork.

The collected data predicts the growth in bandwidth requirements that isbased upon current and past performance. This enables the planningability for future expansion in hardware and software requirement.

The platform of the present invention is intuitive and easy to use as itpredicts and executes performance enhancement and scalability functions.The platform provides an onramp to a customer evolving to full SDN(Software Defined Networking). Customers can use a variety of existinghardware and accomplish centralized SDN control and visibility throughthe use of our software.

SUMMARY OF THE INVENTION

In one aspect of the present invention, a computer-implemented method ofoptimizing data flow through a plurality of networks includes providingan online platform providing a machine learning module; interconnectingthe online platform and a plurality of network providers via at leastone computer having software for causing the at least one computer toperform the following process steps: (a) acquiring a list of openshortest path first (OSPF) neighbors; (b) acquiring a list of labeldistribution paths (LDP) neighbors; (c) executing for each OSPF neighbora bandwidth test for collecting latency information thereof; (d)prompting the machine learning module to predict multiple path outcomesthrough the plurality of networks and calculate a confidence score ofeach path outcome based on latency information for associated OSPFneighbors; and (e) making routing decisions for each OSPF neighbor basedon its respective confidence score.

In another aspect of the present invention, the computer-implementedmethod provides latency information that includes packet loss patternsthrough each OSPF neighbor, wherein each instance of packet loss is timestamped so that the software identifies sequential loss or maximumthroughput of the each OSPF neighbor, wherein the confidence score isbased upon historical information, further including the step ofinspecting the confidence score via the software to tell the machinelearning module whether it is correct or not, wherein if the confidencescore is higher than 60 percent, then 80 percent of the collectedlatency information of step (c) is used by the software to determine areliability value of each respective OSPF neighbor, wherein if theconfidence score is less than 60 percent, then 50 percent of thecollected latency information of step (c) is used by the software todetermine a reliability value of each respective OSPF neighbor, andwherein OSPF neighbors comprises a list of objects including a routerID, a LSA types and an advertisement.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdrawings, description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an exemplary embodiment of the presentinvention; and

FIG. 2 is a continuation of FIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description is of the best currently contemplatedmodes of carrying out exemplary embodiments of the invention. Thedescription is not to be taken in a limiting sense, but is made merelyfor the purpose of illustrating the general principles of the invention,since the scope of the invention is best defined by the appended claims.

Broadly, an embodiment of the present invention provides amachine-learning optimization of a plurality of networks. Themachine-learning optimization includes interconnecting an onlineplatform providing a machine learning module, a core network ofnetworking devices such as routers, servers, firewalls and switchesdeploying novel software, and a plurality of Internet network serviceproviders. The platform collects, via the software, performance data ofthe Internet networks, which the machine learning module utilizes toenhance performance and reduce the latency therein networks by takinginto account thousands of real-time and historic latency and bandwidthmetrics. Thereby the software continually selects an optimal paththrough the plurality of Internet networks.

The present invention may include at least one computer with a userinterface, wherein the user interface may include a touchscreen or otherinput device and output device layered on the top of an electronicvisual display of an information processing system. The informationprocessing system may include Software Defined Networking (SDN) devices.The SDN may be a controller or other centralized device that allows formanagement and control of the network elements. The computer may includeat least one processing unit coupled to a form of memory including, butnot limited to non-user-interface computing devices, such as a serverand a microprocessor, and user-interface computing devices, such as adesktop, a laptop, smart device, such as a tablet, a smart phone, smartwatch, or the like, or a network device such as a router, server, switchor firewall. The software may include a program product including amachine-readable program code for causing, when executed, the computerto perform steps. The program product may include software which mayeither be loaded onto the device or accessed by the computer. The loadedsoftware may include an application on a smart device. The software maybe accessed by the computer using a web browser. The computer may accessthe software via the web browser using the internet, extranet, intranet,host server, internet cloud, wifi network, and the like. The softwaremay include management software on a SDN (Software Defined Network)controller governing flow of data or a path. The SDN controller may be anetwork controlled by coded computing devices that impact and manage thenetwork devices.

Referring to FIGS. 1 and 2, the present invention may include acquiringa list of OSPF neighbors; acquiring a list of LDP (Label DistributionProtocol) neighbors; executing individual OSPF neighbor actions;creating and/or updating of machine-learning module array; making arouting decision; optimizing a path recalculation; and repeating theprocess. Neighbors may be a list of objects such as: router ID, LSAtypes and advertisement. There are 200 other factors within OSPF (RFC2328) of the plurality of network providers. For LDP neighbors thepresent invention may look at the LSR ID and VPN v4 advertisement. Thisis part of the BGP functionality within MPLS architecture. A method ofusing the present invention may include the following. The software,platform and machine learning module disclosed above may be provided.The machine learning module or architecture underlying the core of thenetwork is aware of and directly impacts a multitude of factors to theabove fundamentally basic architecture.

In step 1, the present invention may acquire a list of OSPF neighbors.OSPF is an interior gateway protocol (IGP) for routing Internet Protocol(IP) packets solely within a single routing domain, such as anautonomous system. It gathers link state information from availablerouters and constructs a topology map of the network. The topology ispresented as a routing table to the Internet layer which routes packetsbased solely on their destination IP address. OSPF supports InternetProtocol Version 4 (IPv4) and Internet Protocol Version 6 (IPv6)networks and supports the Classless Inter-Domain Routing (CIDR)addressing model.

In step 2, the present invention may acquire a list of LabelDistribution Paths (LDP) neighbors. The Label Distribution Protocol(LDP) is a protocol defined by the IETF (RFC 5036) for the purpose ofdistributing labels in an MPLS environment. LDP relies on the underlyingrouting information provided by an IGP in order to forward labelpackets. The router forwarding information base, or FIB, is responsiblefor determining the hop-by-hop path through the network. Unlike trafficengineered paths, which use constraints and explicit routes to establishend-to-end Label Switched Paths (LSPs), LDP is used only for signalingbest-effort LSPs.

In step 3, the present invention may execute for each OSPF neighbor anICMP echo ping with scale pattern to obtain latency information. Theinformation is used for both ingress and egress, and capturesminimum/maximum, averages, standard deviation and other statisticalinformation regarding of latency, thereby collecting, quantifying andcapturing latency, packet loss, and jitter. Each OSPF neighbor may use abandwidth testing probe with a priority of 8 (8 is “best effort” inpriority) so that the bandwidth test will not interfere with currentbandwidth usage and traffic passing through the circuit. While thebandwidth test runs, the software of the present invention captures theout interface total bandwidth. The same may be done for the upstreamonce the download is measured. The software of the present inventionextract packet loss patterns from the bandwidth test, whereby eachinstance of packet loss is time stamped so that the software of thepresent invention can identify sequential loss or maximum throughput ofthe circuit.

In step 4, the present invention may create and/or update a machinelearning module array wherein all of the previously gathered informationis captured in a time stamped envelope. Based upon multipleinstances/envelops of data, the machine learning module may be promptedto predict multiple outcomes, wherein each outcome may include a“confidence” or percentage. In certain embodiments, the OSPF cost of thenetwork is then impacted directly based upon the “confidence” orpercentage.

In step 5, the present invention may make routing decisions based atleast in part on the related machine learning confidence. The confidence(or confidence score) may be based upon historical information. It isthen inspected by the software to tell the machine learning modulewhether it is correct or not. Over time, confidence is impacted todetermine the value or reliability of a given path or route in therelevant network. Wherein if the machine learning confidence is higherthan 60%, we use the 80% of the calculated metrics based on step 3. Ifthe machine learning confidence is less than 60%, the present inventiononly uses 50% of the confidence metric. The present invention may beadapted so that at every predetermined internal, for example every oneminute, 10% of the remaining metrics are weighed for the following, say,five minutes to determine if the confidence within the selected path isacceptable. This function is due to either lack of historical data orpoor historical performance.

In step 6, the present invention may recalculate the optimal path. Ifthe immediately previous optimal path is no longer optimal, the presentinvention may be adapted to remove the traffic and route to thesecondary calculated optimal path based on an OSPF cost.

The machine learning module may be only a portion of that. The softwareof the present invention may not rely solely on machine learning alonebut upon our optimization options within step 3, thereby enabling thesoftware to quality control (double check) the machine learning results.Especially since in initial stages of deployment and instances wherethere is not data yet gathered, the machine learning module may not yetbe equipped to make any sort of educated decision

Steps 1 through 6 may be repeated every predetermined period, forexample every five minutes. The constant gathering of performance datadone both in real-time as well as stored historically in the databaseenables rapid network route and performance optimization based onmachine learning that can be done across large private networks as wellas the Internet. Via the Internet and over the plurality of global datacenters wherein the software of the present invention is deployed, thepresent invention is enable to leverage the above-mentioned softwarealgorithm to navigate routes and control performance of data traversingpotentially every Internet Service Provider across the globe.

Regarding the deployment of the present invention, the software mayreside on servers provided by the plurality of global data centers,wherein it may be front-ended by a Software Defined Network (SDN)Management platform and related portal. An individual using thisplatform would simply use their browser to log on to the portal, createan account, and determine what wide area network services they wish topurchase such as MPLS, VPLS, or EoIP. This is done by simply clicking onthe desired service icon and agreeing to the terms and conditions of theservice. These services are then deployed on top of the underlyingInternet or private infrastructure that is completely controlled,optimized and managed via the aforementioned software.

Unlike traditional MPLS, VPLS, or Private Line networks, the underlyingfabric is constantly being optimized. Over the Internet a given path mayleverage a path traversing three different Internet Service Providersdue to that being the optimal path. Later that same flow of data maytraverse five such Internet Service Providers if latency, throughput orreliability is more advantageous based upon the software and machinelearning module decision. A user is completely unaware of the fluidsoftware performance optimization. All they will see is a stable andreliable MPLS, VPLS, or Private Network connection.

Also, the software of the present invention can be deployed oneequipment located on a local or on site customer premises. Metrics fromthe local device can be coupled with the software described herein tofurther optimize performance. The software can also control and theperformance of IoT or Internet of Things-based services up to andincluding, network traffic aggregation and optimization of data flows tothe respective aggregation point or data center.

The computer-based data processing system and method described above isfor purposes of example only, and may be implemented in any type ofcomputer system or programming or processing environment, or in acomputer program, alone or in conjunction with hardware. The presentinvention may also be implemented in software stored on acomputer-readable medium and executed as a computer program on a generalpurpose or special purpose computer. For clarity, only those aspects ofthe system germane to the invention are described, and product detailswell known in the art are omitted. For the same reason, the computerhardware is not described in further detail. It should thus beunderstood that the invention is not limited to any specific computerlanguage, program, or computer. It is further contemplated that thepresent invention may be run on a stand-alone computer system, or may berun from a server computer system that can be accessed by a plurality ofclient computer systems interconnected over an intranet network, or thatis accessible to clients over the Internet. In addition, manyembodiments of the present invention have application to a wide range ofindustries. To the extent the present application discloses a system,the method implemented by that system, as well as software stored on acomputer-readable medium and executed as a computer program to performthe method on a general purpose or special purpose computer, are withinthe scope of the present invention. Further, to the extent the presentapplication discloses a method, a system of apparatuses configured toimplement the method are within the scope of the present invention.

It should be understood, of course, that the foregoing relates toexemplary embodiments of the invention and that modifications may bemade without departing from the spirit and scope of the invention as setforth in the following claims.

What is claimed is:
 1. A computer-implemented method of optimizing dataflow through a plurality of networks, comprising: providing an onlineplatform providing a machine learning module; interconnecting the onlineplatform and a plurality of network providers via at least one computerhaving software for causing the at least one computer to perform thefollowing process steps: (a) acquiring a list of open shortest pathfirst (OSPF) neighbors of the plurality of network providers; (b)acquiring a list of label distribution paths (LDP) neighbors; (c)executing for each OSPF neighbor a bandwidth test for collecting latencyinformation thereof; (d) prompting the machine learning module topredict multiple path outcomes through the plurality of networks andcalculate a confidence score of each path outcome based on latencyinformation for associated OSPF neighbors; and (e) making routingdecisions for each OSPF neighbor based on its respective confidencescore.
 2. The method of claim 1, wherein the latency informationincludes packet loss patterns through each OSPF neighbor, wherein eachinstance of packet loss is time stamped so that the software identifiessequential loss or maximum throughput of the each OSPF neighbor.
 3. Themethod of claim 1, wherein the confidence score is based upon historicalinformation.
 4. The method of claim 1, further comprising the step ofinspecting the confidence score via the software to tell the machinelearning module whether it is correct or not.
 5. The method of claim 1,wherein if the confidence score is higher than 60 percent, then 80percent of the collected latency information of step (c) is used by thesoftware to determine a reliability value of each respective OSPFneighbor.
 6. The method of claim 1, wherein if the confidence score isless than 60 percent, then 50 percent of the collected latencyinformation of step (c) is used by the software to determine areliability value of each respective OSPF neighbor.
 7. The method ofclaim 1, wherein OSPF neighbors comprises a list of objects including arouter ID, a LSA types and an advertisement.